{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Matplotlib Tips\n",
    "\n",
    "Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Matplotlib\n",
    "\n",
    "Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``plt`` interface is what we will use most often, as we shall see throughout this chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Styles\n",
    "\n",
    "We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures.\n",
    "Here we will set the ``classic`` style, which ensures that the plots we create use the classic Matplotlib style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this section, we will adjust this style as needed.\n",
    "Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available.\n",
    "For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``show()`` or No ``show()``? How to Display Your Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\n",
    "The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting from an IPython shell\n",
    "\n",
    "It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
    "IPython is built to work well with Matplotlib if you specify Matplotlib mode.\n",
    "To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:\n",
    "\n",
    "```ipython\n",
    "In [1]: %matplotlib\n",
    "Using matplotlib backend: TkAgg\n",
    "\n",
    "In [2]: import matplotlib.pyplot as plt\n",
    "```\n",
    "\n",
    "At this point, any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\n",
    "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``.\n",
    "Using ``plt.show()`` in Matplotlib mode is not required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 是否在Ipython中显示图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 10, 100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dxDdCQ9XmNFOm6E7iHa4v/ocPq83ZIyN1J/GdUqWgXz/3vmgNPSZOhJEjb/6YfWf3cfD8QZ/k8YXsrh83zvh1ffGfOFG1hJ2yObtVsrt+3PiiNXzvwAG1YUtum7NP3TGV99b9eZtHp7rrLjVKcNs23Ums5+rin5mpxureqMvHzTp1gqQkd75o7S7hYgJv/fiW7hiWmjQJhg3LfcOWe5vfy4ydM7iaftU3wbwsIEBd7bhxsTdXF/81a9Q63W3a6E7iewEB6kPP3Pj1vak7prpqHZ/MTHUFff/9uT+2ZumaNK3QlIXx7tkRffhwtTpAWpruJNZydfHPbvXndbDF1B1TWbbPPWMkR4xQQ1zd9qK1Myklk2Mmu2pG79q1ULQotGqVt8ePbD6Sib9M9G4oH6pXD2rVUoMo3MS1xT85GebNg7/8Je/PSctI4+PNH3svlI/VqgX168PSpbqT+I+YkzFcunqJ9tXb645imQkTVKs/r42ogY0G8uOhH0m87J4NJu69Vy1r4SauLf6LFv2+0mVeDWg0gB8P/cipy+4ZIzl8uBn140uTYyYzotkI1yziduWKmiczfHjen1MipARf9fvKVcs9DBmiasqlS7qTWMcdr9AcTJmSv1Y/qBdtvwb9mB473TuhNBgyRE1UcdOL1q4yZSbTY6czotkI3VEss3ChmiCZn0YUwOCwwa5ZzwigXDm48071QegWlhR/IURPIUScEGKvEOLlHH7eWQhxQQixPevPP604742cOwerVhVst677mt3HpBj33NovW1a9aOfN053E/QJEAJv/bzMNyzbUHcUy06bBPffoTmEPI0a4q+vH4+IvhAgEPgF6AY2Be4QQjXN46BopZYusP296et6bmTMHunVTk53y665ad5FwMcFV6/ybrh/fqVyisu4Iljl/XjWi+vfXncQewsPV6sDHjulOYg0rWv5tgb1Syv1SylRgOhBhwXELbMqU/PVRXiswIJCYx2KoVaaWtaE0Cg+HjRvh5EndSQwnmTMHunaF0qV1J7GHIkXUB+G0abqTWMOK4l8FOHLN1wlZ37teOyHEL0KI74UQYRacN0dHj8L27dC7d8GP4aa+SlCbU/TrBzNm6E5iOIkVXT5SSlIzUq0JZAPDh8PUqbpTWCPIgmPkdEv/+kUFtgE1pJRJQojewDyg3o0OOHr06N/+3rlzZzpfv3j4TUyfrj6d/W05h9wMHw6jRsHTT+tOYjjBiROqi2P+fM+O8/769zl95TRju4+1JphmnTpBQgLs3Qt16+rLER0dTbSHmw0I6eHiL0KIdsBoKeXdWV+/AiClHHOT5xwE2kgpT+fwM+lJptat4d131eWq8bv0dKhSBTZsgNq1dadxl0tXLxGbGEu7au10R7HMuHGq+Hu6rEHMyRj6TevHgWcOuGb465NPQuXK8NprupP8TgiBlDJfY2ut+L+xBagnhKglhCgEDAP+0F4QQlQUWYuaCyHaZp33jAXn/oP4eHUzJh8XCn4jKAgGDIDvvtOdxH2i4qJ4e+3bumNYaupUa0b5NC3flOKFirPhyAbPD2YTQ4eqmfNO53Hxl1KmA08BS4HdwEwp5U4hxGNCiOydPgcBsUKIX4CPgGEeNe9v4Lvv1PDOwEBrjrfr1C4OnT9kzcFsYMgQd7xo7WbGzhkMDRuqO4ZlDhxQ3Rrdunl+LCEE9zS5h2mxLrlLCnToAKdPw549upN4xuNuH6t50u3TogV89BF07GhNln+u/ieXUy/zn7v/Y80BNcvIUF0/a9fq7a90k3PJ56jx3xokPJdAyZCSuuNYYuxY9QHw+efWHG/f2X20G9+OY88fIyjAituM+j37LJQpo+6j2YGubh9biI9XQxnbW7ikyrAmw5ixcwaZMtO6g2oUGKiujEzXj3Xm7ZlH19pdXVP4Qb0+hgyx7nh1QuvQvU53jl1yyQB51O9nxgxn75fhmuL/3XcwaJB1XT4Ajcs1JrRIKOsOr7PuoJqZrh9rubHL5/Bh666es00ZMIXqpapbe1CNbr9d7ZcRG6s7ScG5qvgPHmz9cQc3HsysXbOsP7AmHTqoYXzx8bqTuENEgwj61s9leysHmTVLDZUOckfvjNcEBDi/IeWK4h8fD4mJ1nb5ZBvUeBCzd892VdfPoEGm68cqj9/6OMULFdcdwzLeakS50dChzu76cUXxt3qUz7UalWvEq3e+6qpZik5vsRjeceiQ6vYxQ6Xzpk0bNX8mJkZ3koJxRfGfOdPaG1TXe+LWJygc5J4pw+3bq6FqcXG6kxh2MmsWREaaLp+8EkLNnZk9W3eSgnF88Y+Lg1OnvNPl41YBAWbUj/Fnvujy+XTLp2w5usW7J/GhgQNN8ddm9mz1PyDA8f8S3xowwF0bU/ia3ebHeOrwYTWxq0sX757nbPJZvo1xz6L4t92mlr524oQvx5fMOXMKtmmLv7vzTjhyBA4e1J3EeS5evUiDjxuQlpGmO4plZs+GiAgIDvbueQY1HsSs3bNcM4AiIMC5XT+OLv6HDqkWS4cOvjmflNI1Lb7AQPVmnzNHdxLnWRS/iLqhdQkO9HKl9KFZs9QoMG9rWLYhoUVCXbXWz8CBznwfObr4z52rNirx1Q2qbpO7EXPSobf2czBggDNftLrN3j2bgY3cc7l5/Djs2uW7lXAHNBzA3D3u6XPs0EFdRR9w2OZ/ji7+c+b4dou5VhVbuWrC1113wc6d6s1v5M2VtCss37+ciIZaN6uzVFSU2vyoUCHfnK9/o/7M2T3HNVfRQUHOvIp2bPE/eVKNr/Xluv0DGg1gzh6H/R++iZAQ9aaPitKdxDmW7F1Cm8ptKFu0rO4olpk717eNqOYVmrPivhVkrfLuCk4c9ePY4h8VBb16+XbHrtuq3sa55HPEn3HP2ghO7a/UZfep3a5ay+f8ebXBT8+evjunEILaZdy1o9Bdd6kRP0eP6k6Sd44t/nPmqD5rXwoQAUQ0iGDennm+PbEX3X03bNoEZ8/qTuIMr3V8jUdaP6I7hmUWLlTDO4u7Z4UKLQoVgr59nTV82pHF//x5WL9etfx9bUCjAcSdds/U2GLFVNfZggW6kxg6+LrLx83693dWF6ojN3P59ls1G9FJv2g7+/ZbtUSGp5t1G85y5QpUqgT798Mtt+hO43yXL6vf56FDaqMXX/KbzVx0dPm4WZ8+EB2tXryG/1i2DFq31lf4pZTsOrVLz8m9oFgx1YW2eLHuJHnjuOJ/5QqsWKH61wxrlCkDbduqYmD4j7lz9TaiUjNSaf91e04kndAXwmKRkTDPIbcEHVf8V6zQ21pxq8hI0412M/P2zGPPaQcu4HIDaWnqZm9kpL4MIUEh9Krbi6g97nnh9e2rGlEpKbqT5M5xxT8qSk2oMKwVHq6KQXq67iT29MrKV7h49aLuGJb58UeoXRuqVtWbo3/D/q6a7VuuHLRoAStX6k6SO0cV/4wMNSrFDsX/+KXjTPplku4YlqleHWrUgHXu2a7YMntO7+Hi1Yu0qdxGdxTL2KUR1bNuT9YfWe+qD9b+/Z3R9eOo4r9hg7qbXquW7iQQFBDE098/TUq6A67v8igiwhkvWl+L2hNFRIMIAoSj3i43JKV9in+JkBK0r96eJXuX6I5imYgINXIuI0N3kptz1KvZLi9YgHLFytGsQjNWH1itO4plsvv9bTb6V7t5cfOIbKixc9xiv/yiVnVt0kR3EuXxNo9TLLiY7hiWqVVLNVI3btSd5OYcU/ylVK1SnTeorhfeIJyoOPfcrGraVP2ed+zQncQ+jl86TtzpODrX7Kw7imWyG1F2WVonvEE4fer30R3DUk4Y9eOY4r97N1y9Ci1b6k7yu4gGESyIX+CajSmEcMaL1peKFyrOnKFzKBTooyUvfcBOV9BuFRmphtLa+SraMcU/KkqNSLFLawWg3i31KBlSkq3HtuqOYpmICDPk81olQkq4qtV/5IhvN0DyV82bQ2qqvbd3dEzxt1uXT7bx4eOpWbqm7hiW6dDh9x3SDPeZP18t4+2rDZD8lRCqsWrnJVMcUfyPHYP4eOjUSXeSP7uj2h2UK1ZOdwzLBAWp4rBwoe4khjeYLh/fCQ+391W0I4r/woVqBU9vby5tKHZvsRgFc+GCGoFy9926k+Rszu45fLrlU90xLNOpk9oe8+RJ3Uly5ojiP3++KkiGb9x9t5rsddE9827yLVNmkpaRpjuGpb7/Hu68075r94cWCWX8z+N1x7BMSIh6Ly1apDtJzmxf/C9fVlPRfbnTkL8rUQLat/fvhd42HNlAl4lddMewlN0bUR2qd+Dg+YMcuXBEdxTL2Pkq2vbFf8UKuPVWKF1ad5Kbc1tL0c4vWl+YHzefLjXdU/zT0mDJEujXT3eSGwsKCKJ3vd4sjHfPDadevWD1akhO1p3kz2xf/O3eWsk2ct5IZu6cqTuGZfr1U+uS++tCb1FxUUQ0dM+d0bVroU4dqFxZd5Kb61e/Hwvi3bOtXGgotGplz4XebF38MzPVzV47t1aydarRyVUv2mrV1GJv69frTuJ7cafjuJR6iVaVWumOYpkFC5zxPrq7zt2sO7KO5DQbNpULyK5X0bYu/ps3qyVSa9fWnSR3fer1Yem+pa7r+vHHvX0XxC+gX/1+rlrIzSlX0KUKl+LgMwcpElxEdxTLZL+PMm22EICtX91OecECVCpRiXqh9VhzeI3uKJaxa4vF285cOcOARu7ZJ3TPHjXbtHlz3UnypkwRH2+A62V16qjuny1bdCf5I1P8LRTeIJwFce5pKrdsqUZbxcXpTuJbY7qNoUedHrpjWGb+fNXlY6elUfxNv372u4q2bfHfvx9On1Z7yzpFeINwziSf0R3DMkKoF62dZykauXNKf7+b2bH4C2mzZeeEEFJKyYcfqqWF//c/3Yn82+LFMGYMrHFPb5ZfOXUK6taFxEQ16cjQIyMDKlaEn35SO+ZZTQiBlDJf13a2bfnPn682Qzb0uusutfnHGfdc0PiVxYuhWzfnFf5MmcnGBJvvhpIPgYH2WzPLlsX/wgU10qd7d91JjMKF1QfA4sW6kxgF4eQun8jpkew/t193DMv062evARS2LP5Ll6o1SIq5Z2c3R7Njf6U3TN0xlV2ndumOYZmrV9UM+T4O3CQrQATQp14fVw2g6NFDzZu5dEl3EsWWxd/JrRU36tNHrfOTmqo7ifdIKXl99euumqcRHQ1hYWqujBP1a+Cu2b4lS8Idd9hnzSxbFv/vv3d2f3/CxQS+3Pql7hiWqVgRGjRQC+y51e7Tu0nLSKNZhWa6o1hmwQJnv4+61+7O5qObuZByQXcUy9jpKtqWxb9aNfXHqUICQ3hx+YtcTb+qO4pl7PSi9YYFcWpWr3DJYHgpnbM0yo0UK1SMDtU7sHTfUt1RLJO9ZlZGhu4kNi3+Tn7BApQrVo6w8mFEH4zWHcUy2cXfZiODLTM/fj79Gjj8hXeN2Fg1TyMsTHcSzzx565OEFgnVHcMyNWqoK+lNm3Qnsaj4CyF6CiHihBB7hRAv5/BzIYT4KOvnMUKIm66Y5fTiD+5bnbBZM7XC5+7dupNY79TlU8Qmxrpqo/bs+2ZOv5DpU78P3Wp30x3DUna5iva4+AshAoFPgF5AY+AeIUTj6x7WC6iX9ecR4LObHbN1a09T6de3fl8Wxi/EbpPoCip7tq8dXrRWK1aoGIv+sojCQYV1R7GMGTRhX3Z5H1nR8m8L7JVS7pdSpgLTgesXQo8AJkllI1BaCFHphqFs2RmVP2HlwhBCEJsYqzuKZezyorVa0eCidKjeQXcMyyQmqiu0Tp10JzFy0ratmnl94IDeHFaU2SrAtfuuJWR9L7+PcRUhBNMGTqNaKQffub5Oly5qyY3Tp3UnMW5m0SI1QbJQId1JjJwEBKjh07obUkEWHCOnXsXr+zry8pjfjB49+re/d+7cmc6dOxckl3a3V71ddwRLhYRA165qtMJ99+lOY9zIggUQGak7hXEz/frBp5/C008X7PnR0dFER0d7lMHjhd2EEO2A0VLKu7O+fgVASjnmmsd8AURLKadlfR0HdJZSHs/heNIt/eRu9M03qvh/953uJEZOrl6F8uVh3z4oW1Z3GutM2D6BTJnJgy0f1B3FEklJUKkSHD2qJn95StfCbjFnB+wAABzgSURBVFuAekKIWkKIQsAw4PoVLOYD92WN+rkduJBT4Tfsr08fWL7cHbN9pZRcSbuiO4aloqOhSRN3FX6AMoXLMGXHFN0xLFO8OLRvr3e2r8fFX0qZDjwFLAV2AzOllDuFEI8JIR7LethiYD+wF/gKeMLT8xp6lC8PjRrBDz/oTuK5Xad20fpLFwwtu8aCBc7aACmvutXuxpajW8xsXwvZdj1/N0nLSEMIQVCAFbdY9Hv7bThxAj76SHcSz7yz9h0SLibwce+PdUexhJRqEtGSJdD4+sHWLtBnah/ua3YfQ5sM1R3FEocOQZs26r0UGOjZsVy1nr+b9J3WlxX7V+iOYRm3zPbN3qjdLWJiIDhYXZm5kdsmTtaoAZUrw0ZN2xaY4u8DXWp2cdXStE2aqMK/c6fuJAV36vIpdibuNLN6HaRvfdWIypSZuqNYRmfXjyn+PhDeIJwF8QvMbF8bWfzrYrrW7kpIkMO2uLoJt8/qrVqyKvF/jSdAuKdsmeLvco3KNiI4MJiYkzG6o1jG6cX/TPIZhjQeojuGZU6cgPh4tQmSm5UMsWBcpI3ceqvaInW/hg3LTPH3ASGE6/orO3VS3T6JibqTFMxz7Z5zzY1DULN6e/Qws3qdRudsX1P8faR/w/6cTzmvO4ZlQkLUEgJmb197cHuXj5uFh+sp/maop1FgEyeqDalnz9adxL+lpECFCqrr4JZbdKcx8uvyZTXb98gRKFWqYMcwQz0Nn+rdW20QnpKiO4l/W7UKmjf3n8KflpHG+iPrdcewTLFi0LGjmp/hS6b4GwVWrhw0baqWFDD08bcun7TMNHp+25Nzyed0R7GMjgEUpvgbHgkPV10/TjF712w2H92sO4ZlpHTvkg43UjS4KJ1rdmbxr+654dS3L3z/vdotz1dM8Tc84rTZvmPXjSUpNUl3DMv8/LPqNmjQQHcS38qeO+MWVapArVqwbp3vzmmKv4/FnY5jwvYJumNYpmFDKFwYtm/XnSR3xy8d59ezv3JndfcMhp8/379a/dn61u/L0n1LSc1wwfKyWXzd9WOKv48JIXht1WuumaLupNm+C+MX0rNuT4IDg3VHsYy/Fv+KxSvS4JYGrDm0RncUy/i6C9UUfx+rf0t9ShQqwdZjW3VHsYxT+v3dtpDbkSNw+DC0a6c7iR4vtn+R4oWK645hmRYtIDkZ4uJ8cz5T/DWIaBDB/DgHVMs8at9ejTE/elR3khu7nHqZ6IPR9KrbS3cUyyxYoGaHBrljpfB8G9BoALdVvU13DMtkX0X7qiFlir8G4Q3CiYqL0h3DMsHB0KsXLFyoO8mNFQosxJIRSyhTpIzuKJbx1y4fNwsPhygflQZT/DW4vertnEg6wYFzB3RHsYwvX7QFERwYzB3V7tAdwzIXL8L69Wo9H8M9unSB2FjfrJllir8GgQGBLPzLQsoXK687imV69oS1a9XG1Ib3LVumuttKlNCdxLBSSIj6QPfFVbQp/pq0rdKWYoWK6Y5hmVKl4PbbYelS3Un8g+nyca+ICN/0+5vib1gmMhLmzdOdwv3S09Vqqn376k5iD+M2jWPyL5N1x7BMr16wejVcueLd85jib1gmPFwVpbQ03Un+6ELKBd0RLLVuHdSsCdWq6U5iD+WLlWda7DTdMSwTGgqtW6tFE73JFH/DMlWrqinqa9fqTvK7uNNxNP+8uWu20AR1dRURoTuFffSq14u1h9dy6eol3VEsExHh/QEUpvhrdiXtCmkZNmsqe8AXL9r8iIqLolfdXgiX7GoupSr+kZG6k9hHyZCStK/enqX73HPDKTxc3fTNyPDeOUzx16zP1D6sOrBKdwzLZBd/uzS0o+KiiGjonmZyTIza+q9JE91J7CWyQSTz9rjnhlOtWmqDnk2bvHcOU/w16123t6tetE2bqv/G2GCv+sTLiexM3EmXml10R7FMdqvfJRcylglvEE70wWjXrJkF3r+KNsVfs8iGkUTFRbnmRSuEfbp+5sfN5+66dxMSFKI7imVMl0/OKpWoxL6n9xEg3FPSIiLU/29vXUW75zflUPVuqUeZImVctcFIZKQ9iv+VtCsMbzpcdwzLHDoECQlwh3smKlvKTR/yoEb8JCfD7t3eOb4p/jbQv2F/V3X9dOigCtXhw3pzPH3b04Q3cM9MqKgotfBXYKDuJIYvCKEaUnPneuf4pvjbwMBGA0nP9OH+bV4WFKSKlJnwZS3T5eN/+vf3XvEXdhv/LISQdstk5N/8+fDBB2Zzd6ucOQO1a8OJE1CkiO40hq+kp0OlSrB1K1SvfuPHCSGQUuZrGIBp+Rte0b272l/21CndSdxh0SK46y5T+HOTnpnOwngbry2eT0FBahkPb1xFm+JveEWRInD33c7Y4csJ5syBAQN0p7C/ABHAIwseIf5MvO4olvFW148p/obXeLO/8mY+2vQRv5z4xfcn9pKkJLXQVz/37EDpNQEigMiGkczZPUd3FMt07w7btsHp09Ye1xR/w2v69IEff1Qbj/hKRmYG/17zb0qEuGeh+++/V/v0li6tO4kzDGw0kNm7Z+uOYZkiRdQHwIIF1h7XFH8bOXrxKK+vel13DMuULKmGfX7/ve/Ouf7IeioWr0jtMrV9d1Ivmz0bBg7UncI5OtboyIFzBzh8QfNYYwt54yraFH8bCS0SykebPyLxsg/2cPORAQNUf7WvzN0zl/4N+/vuhF6WkgJLlphVPPMjODCY8Abhrur66dNHjZyzcqc8U/xtpEhwEXrV7eWqCV/h4Wp3r5QU759LSsmsXbMY1HiQ90/mI8uXQ4sWUN49O376xFNtn6JlxZa6Y1imdGm1befixdYd0xR/m3Fbf2X58tC8uSpi3rbl2BaKBhclrFyY90/mI2aUT8G0qtSKTjU76Y5hqUGD4LvvrDuemeRlM0mpSVT5oAoHnjlAaJFQ3XEs8dFHarTChAnePU96ZjoJFxOoWbqmd0/kI2lpaoLPzz+bXbsMNdqnTh04dgyKXbf9t5nk5QLFCxWna62uzI9zzwD5gQPVSIXUVO+eJyggyDWFH+CHH9Sb3RR+A6BsWbjtNnUPyAqm+NvQuF7jGNZkmO4YlqlSBRo18v6epG5jRvkY17Oy68d0+xg+8eGHsH07fPON7iTOkJGhPjTXroW6dXWncTYppWu28UxMhPr14fjxPy71Ybp9DNsaOFAt9eDtrh+3+OEHVfxN4ffMj4d+ZMBM99wxL18eWrVSI+g8ZYq/4RNVq0LDhrBypfXHTrycyImkE9YfWKOZM2HoUN0pnK9VpVasOrCKM1fO6I5imcGDren6McXf8JnBg1VRs9q4TeN4f/371h9Yk/R0NcRz8GDdSZyveKHi9KjTg7l7NCwy5SX9+6tVXj2dO2OKv42dTznP/nP7dcewzKBB1nf9SCn5btd3rprYtXo11KwJtWrpTuIOQ8OGMmPnDN0xLFOxopo7s2yZZ8cxxd/GFsUv4pklz+iOYRlvdP3EnIwhJT2F26rcZt1BNTNdPtbqXa83W45u4dRl92wuMWSI51fRHhV/IUSoEGK5EOLXrP+WucHjDgohdgghtgshfvLknP4kvEE4Px76kXPJ53RHsYxV/ZXZpsVOY1iTYa4ZzZGWphbwGuSeCxntigYXZWjYUGJOxuiOYplBg2DhQrhypeDH8LTl/zKwUkpZD1iZ9fWNdJFStpBStvHwnH6jREgJutfu7qr+ykGD1EbkV696fiwpJdNjp3NPk3s8P5hNrFwJ9epBjRq6k7jLF/2+oGvtrrpjWKZCBWjbVn0AFJSnxT8CmJj194mA2V7aYkPDhjI9drruGJapWhWaNrVmluKVtCvc3+J+mlVo5vnBbMJ0+Rh5NWwYTPegNHg0yUsIcV5KWfqar89JKf/U9SOEOACcAyTwhZTyy5sc00zyusaVtCtU/k9l4v8aT/li7lja8YsvYNUqmOGee3CWSE1VN/NiYtSHpGHczPnz6grx8GEoXTr/k7yCcnuAEGIFUDGHH72Wj/O0l1IeE0KUB5YLIfZIKX+80YNHjx792987d+5M586d83EqdykaXJR3ur3DlTQPOvdsZtAgePFFuHQJSrhnwy2PLVkCYWGm8Bu5i46OJjo6mkqVYOTIgh3D05Z/HNBZSnlcCFEJiJZSNsjlOaOBJClljgOzTcvfP/Trp7o3RozQncQ+hg6Fu+6CRx/VncRwihkz1JIpS5f6fnmH+UD2585IIOr6BwghigkhSmT/HegBxHp4XsPh/vIXmDpVdwr7uHBBtfzNxC7v2piwkRmx7ulv7NsXNm4s2HM9Lf7vAN2FEL8C3bO+RghRWQiRvedMBWCtEOIXYDOwSEpp0aKkhlOFh8P69XDKPUOvPTJnDnTpAqHu2MLBtjJlJqN/GI1beheKFYPevQv2XI+Kv5TyjJSyq5SyXtZ/z2Z9/5iUsnfW3/dLKZtn/QmTUv7bk3Ma7pD9oi3ImP+F8Qt5avFT1ofSaMoU0wXmC+2qtuNq+lV+PvGz7iiWuaeAI53NDF+HcUuLBQre9TNh+wRXDe88elTtdNa3r+4k7ieEYHjT4Xwb863uKJa5++6CPc8UfweZuH0iL6+42Tw6Z+nRA/bsgYMH8/6cs8lnWb5/OUPChngtl69Nm6YW6ypcWHcS/zC82XCmxU4jPTNddxRLFCpUsOeZ4u8g7aq1Y1LMJFe9aIcOhcmT8/6cGbEz6Fm3J6ULl879wQ5hunx8q2HZhlQtWZVVB1bpjqKVKf4OUv+W+lQvVZ0V+92zH+L996uN3TMz8/b4STGTGNm8gAObbSg2Vm3M3amT7iT+ZdbgWXSp2UV3DK1M8XeYe5vdy+SYfDSVba5NG9XdsXZt7o89n3Ke9Mx0etTp4f1gPvLtt+qGXYB5J/pUjdI1CA4M1h1DK7OHr8OcvnKaOh/VIeFvCZQIccf02Pffh1274OuvdSfxrfR0qF5dbWzfuLHuNIaTmT18/UDZomXp37A/sYnumSc3fLhaxjgpSXcS31qyRK3NYgq/oYNp+Ru20Levmt1a0HVKnKh/f+jTBx5+WHcSw+lMy99wrOwbv/7i5Em1XeMQ94xYdaQLKRfYcGSD7hhamOJv2EK/frBjBxw4oDuJb0yerFr+JUvqTuLfjl46ysCZA10zfDo/TPE3bCEkRI16yan1PyN2Bt//+r3PM3mLlDB+PDz4oO4kRuNyjalRuoarXl95ZYq/YRsPP6yKYvo1jTApJW+teYvCQe6Z/rpxI2RkQIcOupMYAA+1fIivt/vZUDNM8Xe0A+cO8MKyF3THsEzz5mro47X7km5M2MjV9Kt0rtlZWy6rZbf6XbLnvOMNDRtK9MFoTiad1B3Fp0zxd7BKJSox8ZeJHDjnno7yxx+Hzz77/esvt33JI60fQbikUl68CLNn+9eoJrsrEVKC/g37M/GXibk/2EXMUE+He3bJsxQvVJy37npLdxRLpKRAtWqqa+SWKuep9WEt4p+Kp1yxcrqjWWLcOFizRm3UbtjHntN7SEpNok3lNrqjFEhBhnqa4u9wsYmx9Jjcg4PPHqRQYAGX97OZF15Qyx2E/WUiS/YtYdrAabojWUJKaNQIvvwSOnbUncZwE1P8/VSXiV14tPWjDGsyTHcUS/z6K7RvD4cOSTICL1O8UHHdkSyxYgU89xz88ovp7zesZSZ5+alnbnuG73YVYEssm6pXD1q0gNmzhWsKP8DHH8NTT5nCb9iDafm7QEZmBhJJUECQ7iiWmTtXLfi2bp3uJNY4dAhatYLDh9UWloZhJdPy91OBAYGuKvygZvwmJMCWLbqTWOPzz9UIH1P47e/Q+UMkpbp/lUFT/A1bCgqCZ5+F//xHdxLPpaSosf1PPKE7iZEXLyx/gUm/TNIdw+tM8Tds5XzKef71w78ANeN3+fL87fFrR1OmqE1r6tbVncTIiydvfZJxm8eRKfO4vZxDmeJv2MpXW78i7kwcACVKqA+A//5XcygPZGTAu+/Ciy/qTmLkVacanSgaXJQFcQt0R/EqU/xdZn7cfGbudOYMovTMdMZtHsffbv/bb997+mmYNAnOndMYzAPz5kFoqNmj10mEELzS4RXGrB2DmwefmOLvMmUKl+GVla84conaWbtmUbN0TVpXbv3b96pUUTd/v/hCY7ACkhLGjIGXXzbDO52mf8P+nEs5R/TBaN1RvMYUf5e5s8adVC1ZlWk7nDUrNlNm8taPb/FKh1f+9LMXXlDLIly9qiGYB1asgORk9eFlOEtgQCBfh39N7TK1dUfxGlP8Xej1jq/z9tq3ycjM0B0lz9YdXkfxQsXpWbfnn37WtKn6M8lhAzDeeQdeekktVWE4T/vq7alRuobuGF5jJnm5kJSSduPb8Xy75xkcNlh3nDxLTkumSHCRHH+2YQMMGwbx8WrjF7vbvFntSbx3LwQH605juJ2Z5GUA6oXwj47/YNGvi3RHyZcbFX6Adu1U6/+rr3wYyAP//jc8/7wp/IZ9mZa/S2X/Dt2yDj7Atm3Qt69qTRctqjvNja1f//tVSmH3bEBm2Jhp+Ru/EUK4qvCDWhvnjjvg0091J7kxKVU//5tvmsLvJjEnY4g5GaM7hqVM8Tcc5Y034L334NIl3UlytnAhnD8P996rO4lhpa3HtvLk4iddNe7fFH9Dm89/+pxxm8bl6zlhYdC9uz1n/WZkqDH977wDgYG60xhWuq/5fZxPOc/C+IW5P9ghTPH3E6kZqboj/MHZ5LOMih5Fp5r5n/r6xhvw4Ydq1U87mTQJypaF3r11JzGsFhgQyDtd3+HllS87cgJlTkzx9wPJack0/LghCRftUy1HR49mUKNBNKvQLN/PrVNHbfT+/PNeCFZAV67AqFEwdqyZzetWvev1plzRckzc7o6N3k3x9wNFgoswNGwo/1j1D91RANiZuJPpsdN5s8ubBT7GK6+osfQrVlgYzAOjR8Odd8Ltt+tOYniLEIL3e7zPe+vfs8UEyozMDL7++esCX4mYoZ5+4uLVizT6pBHTBk6jYw19u4dLKenxbQ/C64fz19v+6tGx5s+Hv/8dYmL0Tvzatg169YIdO6B8eX05DN9ISk2yxfain//0OVN2TOHH+38kICDADPU0clYypCSf9fmMB6Me5HLqZW050jLTaFu5LY+1eczjY4WHQ/368P/+nwXBCig9XS07/d57pvD7CzsU/tNXTvPP1f/kk96fFHhIt2n5+5n75t5HmcJl+LDXh7qjWGL/fmjbFjZu1LNZyvvvw7JlsHSp6es3fENKyYi5IyhbpOxv7+OCTPIyxd/PnEs+x5GLRwp0o9Wuxo2DiRPVZu++7P7J/uDZvBlqu3fxR8NmJm6fyNh1Y/npkZ8oGqymupsZvkauyhQp46rCD/DUU1CtmroJ7CspKTB0KLz2min8/ixTZvLrmV99dj4pJUv2LWHGoBm/Ff6CMi1/wxXOnoWWLeGTT9T6P94kpernv3QJZsww3T3+bMfJHXSb3I31D66nTmgdbTlMy9+wncTLifSd2tfrN5lDQ2HqVFWUvT3568svYdMm+PprU/j9XdMKTflnx38SOSOSpNQk3XHyxRR/g71n93rluEmpSfSZ2oc2ldtQrFAxr5zjWu3bw3PPQZ8+6krAGzZsgNdfh7lzobj+QR+GDTxx6xPcWvlWHoh6wFFr/5ji7+eOXzpOu/Ht2HBkg6XHTctIY/B3g2lRoQWjOo2y9Ng38/e/Q48e0LMnXLxo7bFjYmDgQNXir1fP2mMbziWE4NM+n3L4wmFeXvGy5R8AmTLT0uNlM8Xfz1UqUYkJEROInBFp2WbV6ZnpPLzgYQJFIJ/1/cynS0sLAe++C7feqq4ALlvU27Rp0+8Lynn7noLhPIWDCrPoL4s4k3yG5PRky447PXY6w+cMt+x4fyClLPAfYDCwE8gE2tzkcT2BOGAv8HIux5SG763cv1KWe7ecnLR9ksfHmvzLZNl9UneZdDXJgmQFk5Eh5f33S9mpk5QnT3p2rFWrpCxXTsqFCy2JZhi5yszMlF/89IWs8F4FuePkjlwfn1U381e/8/sE+cdC3QhoAETfqPgDgcA+oDZQCPgFaHyTY3ryO3OV1atX+/R8OxN3ypr/rSk/2fyJR8fJzMyU6RnpFqUq+O8hPV3KV16RslIlKZcsyf/z09Kk/PBDKcuWldLH/ytuyNevCTtz6+/iXPI5OXjmYNnss2Zy96ndeXpOQYq/R90+UsrdUsq4XB7WFtgrpdwvpUwFpgMRnpzXX0RHR/v0fI3LNWbDQxvoVbeXR8cRQhAYYN2C9gX9PQQGwttvw5QpahTQc8/lfROYLVvgttvUjd01a6Bz5wJFsJyvXxN25pTfxfmU81xNv5qnxx65cISWX7SkQrEKbHp4Ew3LNvRaLl/0+VcBjlzzdULW9wwbqli8IrXK1PrT9+XvV2aA6tePPxPPyv0rfRmvQLp0ge3bITERqleHBx9Us4Gvvy936hR8+63af7dfP3jmGVi1Chp67/1n+IEJ2yfQ9LOmjFkzhp+O/XTTFUGrlKzCxMiJjOs9jsJB3t0HNCi3BwghVgAVc/jRa1LKqDycI6e7fc4ZD2UAsOvULrpN7kb9W+pz9OJRjlw8QoViFXi41cN0rd1Vd7xc3XKLKuwnTqhNVx58EE6ehFKloEQJdaP48GH1QdGrl9onODRUd2rDDZ69/VlaVGzBvD3zuG/ufSReTqR2mdp83Ptj2lZp+4fHBogAn626a8kMXyFENPCClPKnHH7WDhgtpbw76+tXAKSUY25wLPPBYBiGkU8ynzN8c235W2ALUE8IUQs4CgwD/nKjB+f3H2AYhmHkn0d9/kKI/kKIBKAdsEgIsTTr+5WFEIsBpJTpwFPAUmA3MFNKudOz2IZhGIYnbLewm2EYhuF9tpnhK4ToKYSIE0LsFUK8rDuPLkKIakKI1UKI3UKInUKIZ3Rn0k0IESiE+FkIsVB3Fp2EEKWFELOEEHuyXh/tdGfSRQjxt6z3R6wQYpoQwrtDY2xECPG1ECJRCBF7zfdChRDLhRC/Zv23TG7HsUXxF0IEAp8AvYDGwD1CiMZ6U2mTDjwvpWwE3A486ce/i2zPoLoM/d2HwBIpZUOgOX76OxFCVAGeRk0sbYKaSDpMbyqfmoBaNeFaLwMrpZT1gJVZX9+ULYo/ZiLYb6SUx6WU27L+fgn1BvfbeRFCiKpAH+B/urPoJIQoCXQExgNIKVOllOf1ptIqCCgihAgCigLHNOfxGSnlj8D169ZGABOz/j4RiMztOHYp/mYiWA6EEDWBlsAmvUm0+i/wImr9KH9WGzgFfJPVBfY/IYT318m2ISnlUeB94DBwHLggpVymN5V2FaSUx0E1IIHyuT3BLsXfTAS7jhCiODAbeFZKafHixM4ghOgLJEopt+rOYgNBQCvgMyllS+Ayebi0d6Os/uwIoBZQGSgmhBihN5Xz2KX4JwDVrvm6Kn50GXc9IUQwqvBPkVLO0Z1Ho/ZAuBDiIKor8C4hxLd6I2mTACRIKbOvAmehPgz8UTfggJTylJQyDZgD3KE5k24nhRCVALL+m5jbE+xS/H+bCCaEKIS6eTNfcyYthFr8fjywW0r5ge48OkkpX5FSVpVS1kS9JlZJKf2yhSelPAEcEUI0yPpWV2CXxkg6HQZuF0IUzXq/dMVPb35fYz4wMuvvI4Fcl97xxQzfXEkp04UQ2RPBAoGv/XgiWHvgXmCHEGJ71vdelVIu1pjJsIe/AlOyGkj7gQc059FCSrlJCDEL2IYaHfcz8KXeVL4jhJgGdAbKZk2yHQW8A8wUQjyE+nAcnOtxzCQvwzAM/2OXbh/DMAzDh0zxNwzD8EOm+BuGYfghU/wNwzD8kCn+hmEYfsgUf8MwDD9kir9hGIYfMsXfMAzDD/1/QsFHgwiSMFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving Figures to File\n",
    "\n",
    "One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\n",
    "Saving a figure can be done using the ``savefig()`` command.\n",
    "For example, to save the previous figure as a PNG file, you can run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('my_figure.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a file called ``my_figure.png`` in the current working directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ls: cannot access 'my_figure.png': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!ls -lh my_figure.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To confirm that it contains what we think it contains, let's use the IPython ``Image`` object to display the contents of this file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('my_figure.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In ``savefig()``, the file format is inferred from the extension of the given filename.\n",
    "Depending on what backends you have installed, many different file formats are available.\n",
    "The list of supported file types can be found for your system by using the following method of the figure canvas object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': 'Postscript',\n",
       " 'eps': 'Encapsulated Postscript',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Interfaces for the Price of One\n",
    "\n",
    "A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. We'll quickly highlight the differences between the two here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MATLAB-style Interface\n",
    "\n",
    "Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n",
    "The MATLAB-style tools are contained in the pyplot (``plt``) interface.\n",
    "For example, the following code will probably look quite familiar to MATLAB users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()  # create a plot figure\n",
    "\n",
    "# create the first of two panels and set current axis\n",
    "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n",
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "# create the second panel and set current axis\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `plt.gcf?` Get the current figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  `plt.gca` Get the current figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c6bbc199d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that this interface is *stateful*: it keeps track of the \"current\" figure and axes, which are where all ``plt`` commands are applied.\n",
    "You can get a reference to these using the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines.\n",
    "\n",
    "While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\n",
    "For example, once the second panel is created, how can we go back and add something to the first?\n",
    "This is possible within the MATLAB-style interface, but a bit clunky.\n",
    "Fortunately, there is a better way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Object-oriented interface\n",
    "\n",
    "The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\n",
    "Rather than depending on some notion of an \"active\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit ``Figure`` and ``Axes`` objects.\n",
    "To re-create the previous plot using this style of plotting, you might do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create a grid of plots\n",
    "# ax will be an array of two Axes objects\n",
    "fig,ax = plt.subplots(2)\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more simple plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\n",
    "Throughout this chapter, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\n",
    "In most cases, the difference is as small as switching ``plt.plot()`` to ``ax.plot()``, but there are a few gotchas that we will highlight as they come up in the following sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
